Sampling-based Motion Planning in Configuration and State Spaces: Using supervised learning tools

Robotic systems are the workhorses in practically all automated applications. Manufacturing industries, warehouses, elderly care, disaster rescue and (unfortunately) warfare are example applications where human life has benefited from robotics. By precisely planning controlling their motions via computer programs, real world tasks can be...

IOC based trajectory generation to increase human acceptance of robot motions in collaborative tasks

Collaboration between humans and robots is an important aspect of Industry 4.0. It can be improved by incorporating human-like characteristics into robot motion planning. It is assumed that humans move optimal with respect to a certain objective or cost function. To find this function, also for a robot, we use an inverse optimal control...

Integrating different levels of automation: Lessons from winning the Amazon Robotics Challenge 2016

This article describes Team Delft's robot winning the Amazon Robotics Challenge 2016. The competition involves automating pick and place operations in semi-structured environments, specifically the shelves in an Amazon warehouse.<br/>Team Delft's entry demonstrated that current robot technology can already address most of the challenges in...